Data mining-based algorithm for storage location assignment in a randomised warehouse
Data mining-based algorithm for storage location assignment in a randomised warehouse
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DOI：
10.1080/00207543.2016.1244615
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发表时间：
2017-07
影响因子：
9.2
通讯作者：
K. Pang;H. Chan
中科院分区：
文献类型：
--
作者：
K. Pang;H. Chan
数据挖掘技术在信息提取中的应用已经有很长的历史，它在市场营销中的客户关系管理等领域有着广泛的应用。在零售业中，当顾客频繁地一起购买类似产品时，该技术用于提取消费者的购买行为;在仓储中，也有利于将这些相关产品就近存储，以减少拣货操作时间和成本。在本文中，我们提出了一个基于数据挖掘的算法，在一个随机拣货员到零件仓库中的拣货项目的存储位置分配，通过提取和分析客户订单中不同产品之间的关联关系。该算法的目的是最大限度地减少总行程的距离，为两个放远和订单拣货操作。广泛的计算实验的基础上，模拟在香港的计算机和网络产品备件仓库的操作的合成数据进行了测试所提出的算法的有效性和适用性。结果表明，我们提出的算法是更有效的最近的开放位置和纯粹的专用存储分配系统，在最小化总的旅行距离。通过模拟大规模仓库操作的实验，进一步评估了所提出的存储分配算法。在三种存储方法之间的性能比较上观察到类似的结果。它支持所提出的存储分配算法，并适用于提高仓储作业效率，如果物品之间有很强的关联。
Data mining has long been applied in information extraction for a wide range of applications such as customer relationship management in marketing. In the retailing industry, this technique is used to extract the consumers buying behaviour when customers frequently purchase similar products together; in warehousing, it is also beneficial to store these correlated products nearby so as to reduce the order picking operating time and cost. In this paper, we present a data mining-based algorithm for storage location assignment of piece picking items in a randomised picker-to-parts warehouse by extracting and analysing the association relationships between different products in customer orders. The algorithm aims at minimising the total travel distances for both put-away and order picking operations. Extensive computational experiments based on synthetic data that simulates the operations of a computer and networking products spare parts warehouse in Hong Kong have been conducted to test the effectiveness and applicability of the proposed algorithm. Results show that our proposed algorithm is more efficient than the closest open location and purely dedicated storage allocation systems in minimising the total travel distances. The proposed storage allocation algorithm is further evaluated with experiments simulating larger scale warehouse operations. Similar results on the performance comparison among the three storage approaches are observed. It supports the proposed storage allocation algorithm and is applicable to improve the warehousing operation efficiency if items have strong association among each other.